Anti-GRP78 autoantibodies induce endothelial cell activation and accelerate the development of atherosclerotic lesions
Bibliographic record
Abstract
The 78-kDa glucose-regulated protein (GRP78) is an ER molecular chaperone that aids in protein folding and secretion. However, pathological conditions that cause ER stress can promote the relocalization of GRP78 to the cell surface (csGRP78), where it acts as a signaling receptor to promote cancer progression. csGRP78 also possesses antigenic properties, leading to the production of anti-GRP78 autoantibodies, which contribute to tumor growth. In contrast, the presence and role of anti-GRP78 autoantibodies in atherosclerosis is unknown. Here, we show that atherosclerotic-prone ApoE-/- mice develop circulating anti-GRP78 autoantibodies that bind to csGRP78 on lesion-resident endothelial cells. Moreover, GRP78-immunized ApoE-/- mice exhibit a marked increase in circulating anti-GRP78 autoantibody titers that correlated with accelerated lesion growth. Mechanistically, engagement of anti-GRP78 autoantibodies with csGRP78 on human endothelial cells activated NF-κB, thereby inducing the expression of ICAM-1 and VCAM-1, a process blocked by NF-κB inhibitors. Disrupting the autoantibody/csGRP78 complex with enoxaparin, a low-molecular-weight heparin, reduced the expression of adhesion molecules and attenuated lesion growth. In conclusion, anti-GRP78 autoantibodies play a crucial role in atherosclerosis development, and disruption of the interaction between anti-GRP78 autoantibodies and csGRP78 represents a therapeutic strategy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".